Abstract

Global Navigation Satellite Systems (GNSS) provide valuable information for positioning and navigation of agricultural vehicles. In the last decade, there has been an increase of these navigation systems for precision agriculture applications that require the positioning, attitude, or guidance of farm vehicles. However, to ensure safe navigation, it is crucial to take into account the environment where agricultural vehicles typically work, as well as the type of errors that may occur. This work proposes a practical solution to enhance the free signal acquired by a DGPS (Differential Global Positioning System) receiver through the identification and attenuation of principal error components and software-based filtering of avoidable errors with the final purpose of improving the behavior of a farm robot developed to create crop maps of vineyards. The implemented algorithm is based on the real-time analysis of NMEA (National Marine Electronics Association) strings in such a way that time, position, and quality indicators come from processing GGA (GPS Fix Data) messages, whereas ground speed and heading (dynamic states) are extracted from VTG (Course and ground speed) messages. By separating the source of information and applying different conditioning operations, the processing time diminishes at the same time reliability improves, being both the consequence of reducing complexity and better adjusting the filter to the signal. Field tests conducted in the spring of 2015 showed the advantages of filtering raw GPS data with the algorithm proposed, and the convenience of its implementation in autonomous robots where GPS data plays a key role for both navigation and mapping.